ArticleAccess microbiology2023
Comparative analysis of clinical breakpoints, normalized resistance interpretation and epidemiological cut-offs in interpreting antimicrobial resistance of
Article in Access microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 3 citations in OpenAlex.
- Occurrence of Phenotypic Multidrug-ResistantAntibiotics (Basel, Switzerland) · 2026Article
- Phenotypic and genomic analysis of antimicrobial resistantFrontiers in microbiology · 2026Article
- Interpreting antimicrobial resistance from bacterial whole-genome sequencing: prediction tools, database fragmentation, analytical trade-offs, and harmonized reporting.Frontiers in microbiology · 2026Review
- Identification and Antimicrobial Potential of Marine Sponges (International journal of microbiology · 2025Article
- Towards harmonized laboratory methodologies in veterinary clinical bacteriology: outcomes of a European survey.Frontiers in microbiology · 2024Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Existing breakpoint guidelines are not optimal for interpreting antimicrobial resistance (AMR) data from animal studies and low-income countries, and therefore their utility for analysing such data is limited. There is a need to integrate diverse data sets, such as those from low-income populations and animals, to improve data interpretation. Gap statement: There is very limited research on the relative merits of clinical breakpoints, epidemiological cut-offs (ECOFFs) and normalized resistance interpretation (NRI) breakpoints in interpreting microbiological data, particularly in animal studies and studies from low-income countries. Aim: The aim of this study was to compare antimicrobial resistance in Methodology: A total of 59 non-repetitive poultry isolates were selected for investigation based on lactose fermentation on MacConkey agar and subsequent identification and confirmation as Results: According to the interpretation of all breakpoints except ECOFFs, tetracycline resistance was significantly higher (TET) (67.8 -69.5 %), than those for ciprofloxacin (CIPRO) (18.6 -32.2 %), imipenem (IMI) (3.4 -35 %) and ceftazidime (CEF) (1.7 -45.8 %). Prevalence estimates of AMR using CLSI and NRI bespoke breakpoints did not differ for CEF (1.7 % CB and 1.7 % CO Conclusion: AMR interpretation is influenced by the breakpoint used, necessitating further standardization, especially for microbiological breakpoints, in order to harmonize outputs. The AMR ECOFF estimates in the present study were significantly higher compared to CLSI and NRI.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.